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Curious about how vector databases are transforming AI-driven apps? 🤔 By using embeddings, vector #dbs enable retrieval-augmented generation (#rag) to access and organize vast data more efficiently. This blend of natural language understanding with rapid retrieval is opening doors for smarter search and recommendations, driving innovation across industries. 🚀 If you plan to pursue #AI apps at scale, you will likely need to adjust your data architecture to support the demands of #LLMs. Here are some of the best options: - Pinecone Optimized for real-time similarity search. Pinecone is scalable, fast, and integrates easily with machine learning models to handle high-dimensional vector data. Available in #AWS Cloud/Marketplace - https://lnkd.in/eJksizJM - Qdrant Designed for efficient similarity search with filtering, making it highly effective in RAG pipelines. Qdrant supports distributed search across clusters, which is crucial for large-scale data. Redis and ElasticSearch also have vector support but can be costly to scale (memory/performamce). #AI #DataScience #RAG #CTO
